In practice, \(X^n\)  can be taken any arbitrary distribution. In this section, we first extend our results to \(m\) -order Markov chain. Then, we provide a general bound which works for any distribution of \(X^n\)  based on the spectral methods in [1]} and apply it to bound the generalization error when the dataset is a mixture of many Markov chains.
